Abstract

BACKGROUND: The large, expanding literature on biomarkers is characterized by almost ubiquitous significant results, with claims about the potential importance, but few of these discovered biomarkers are used in routine clinical care.

CONTENT: The pipeline of biomarker development includes several specific stages: discovery, validation, clinical translation, evaluation, implementation (and, in the case of nonutility, deimplementation). Each of these stages can be plagued by problems that cause failures of the overall pipeline. Some problems are nonspecific challenges for all biomedical investigation, while others are specific to the peculiarities of biomarker research. Discovery suffers from poor methods and incomplete and selective reporting. External independent validation is limited. Selection for clinical translation is often shaped by nonrational choices. Evaluation is sparse and the clinical utility of many biomarkers remains unknown. The regulatory environment for biomarkers remains weak and guidelines can reach biased or divergent recommendations. Removing inefficient or even harmful biomarkers that have been entrenched in clinical care can meet with major resistance.

SUMMARY: The current biomarker pipeline is too prone to failures. Consideration of clinical needs should become a starting point for the development of biomarkers. Improvements can include the use of more stringent methodology, better reporting, larger collaborative studies, careful external independent validation, preregistration, rigorous systematic reviews and umbrella reviews, pivotal randomized trials, and implementation and deimplementation studies. Incentives should be aligned toward delivering useful biomarkers.

Progress in unraveling the molecular basis of diseases and advances in technology have fueled the search for novel biomarkers in many diseases. There is hope that biomarkers will improve our ability to identify, manage, or prevent a wide range of conditions that jeopardize health.

Research in this field has expanded over the years to include measurements of increasing numbers of proteins (the more typical type of biomarker) and other types of molecules (metabolites, DNA genetic variants, different types of RNA molecules) that may serve as biomarkers. For proteins, mass spectrometry allows measurement of multiple analytes with possibly high sensitivity and selectivity, at impressive speed. Multiple peptides, proteins, and their isoforms can be analyzed simultaneously, seemingly allowing even more refined classifications of patients into different classes, or the monitoring of patients during the course of their disease or the management thereof (1). Similarly, recent technical advances in metabolome, genome, and transcriptome measurements have been impressive and raise new methodological and clinical challenges for harnessing this information (2, 3). Biomarkers are typically measured in biospecimens, but an expanded definition includes also other bioinformation, e.g., procured by imaging (4), sensors, or other measurement tools.

Despite enthusiasm and high prospects for biomarkers, this measurement revolution has not yet resulted into tangible health benefits. Several commentators have pointed to the fact that, despite massive investments of resources, the biomarker business has added very little to everyday clinical medicine so far (5–7). An evaluation of the indications and contraindications of all drugs considered by the European Medicines Agency (EMA) and published in 883 European public assessment reports and pending decisions found mentions of only 37 predictive biomarkers for 41 drugs (8).

Previous authors have already provided possible explanations for this failure to deliver, and some have offered potential accompanying solutions to remedy this situation (9–11). Most of the previous literature has focused on technical challenges pertaining to the analytical capacity and limitations of existing measurement methods or the difficulty in finding laboratory professionals with sufficient expertise in applying these laboratory methods. Calls are often made for the investment of even more resources into biomarker research, and several countries have launched new funding schemes, for biomarker research in general, or for programs for specific diseases and conditions. However, it is questionable whether overcoming the technical limitations or perpetually reigniting interest in biomarkers through new bandwagon terms (e.g., personalized medicine, precision medicine, etc.) would suffice to make biomarker research clinically useful.

There are additional explanations, beyond technical caveats, that can account for the current failures to deliver that plague biomarker research. Some of these may be specific to biomarkers and they shape the difficult balance between hope and hype in this field (12–14). Many others are not unique for biomarker research. Similar problems are highly prevalent also in other areas of biomedical investigation, where they have been held responsible for the fact that 85% of research investment seems to be wasted (15–17). Actually, the 85% estimate for waste does not mean that 15% of research yields useful results. A 15% hit rate would have been outstanding by investment or start-up standards. The 15% refers to the fraction of resources and effort directed toward research that can have a chance of some success, but the hit rate even for this 15% may be very small. In trying to optimize the overall hit rate, the more fundamental problems for biomarkers may have to do with the way in which the biomarker pipeline is currently constructed.

Below we discuss each of the elements in the current biomarker pipeline and the pipeline as a whole, trying to dissect the main reasons for the failure of biomarkers and exploring whether these failures can potentially be overcome.

Discovery

The biomarker pipeline is typically presented as a series of connected pipes, through which biomarkers evolve, from a discovery phase to validation, clinical translation, evaluation, and implementation.

In a typical biomarker discovery study, a large number of candidate biomarkers are evaluated in a sample of cases, who have the target disease or condition of interest, and noncases, who do not have the targeted condition. Then some measure is calculated to express the strength of the association between a single biomarker, or a panel of markers, and the condition of interest.

Many marker discovery studies suffer from substandard methodology. Some problems may be simple analytical flaws and laboratory errors, as in the case of the most cited biomarker paper ever, a study that claimed to have identified a perfectly sensitive and specific proteomic signature to detect ovarian cancer—subsequently found to reflect technical problems with the sequence of testing the samples while the measurement machinery was malfunctioning (18, 19). More frequent are issues with the design of biomarker discovery studies and the way in which they are analyzed; such problems have been described in many empirical evaluations of large numbers of biomarker studies (20–24). Most (not all) of these deficits are likely to exaggerate biomarker performance.

Discovery studies often analyze a large number of markers in parallel, which increases the risk of falsely positive results. Although statistical methods exist to account for multiplicity, these methods are not always applied and, if performed, they are not always correctly done or reported. In general, sample sizes are fairly limited. There is little attention to the selection of cases and noncases. Evaluation of extreme cases, e.g., selecting patients with high disease severity, may not reflect the more typical patients (mostly with more moderate disease severity) unless the biomarker is specifically intended to be used only in high severity triage.

Pepe and colleagues offered 2 explanations for the limited attention given to the design of biomarker discovery studies (25, 26). They pointed to the exploratory nature of such discovery studies and argued that traditionally scientists have not demanded rigor in exploratory research.

Yet there are more problems than loose methods associated with the exploratory nature of biomarker discovery studies. It is very likely that a large number of biomarker discovery explorations are not reported at all. In a set of studies registered in ClinicalTrials.gov, a report in a peer-reviewed journal could be found within 18 months after study completion for less than 60% (Fig. 1) (27, 28). There is also evidence that the time to publication is significantly associated with the performance of the medical test, with shorter times for stronger associations.

Fig. 1.Diagnostic test studies, in general, can take a long time to publish and many remain unpublished.

Kaplan–Meier plot for the time from completion to publication for test accuracy studies registered in ClinicalTrials.gov (excluding those registered after completion). Redrawn with permission from Korevaar et al. (28).

To aggravate matters, published reports are often incomplete. The reported outcome measures deviate from the registered ones, often emphasizing more impressive performance statistics, with an element of “spin” (29, 30). In the absence of preregistration, it is unknown whether all evaluated markers are included as such in the final study report.

This cocktail of poor methods, selective publication, selective and incomplete reporting, aggravated by “spin” in titles, abstracts, and conclusions, is responsible for a considerable amount of waste in the biomarker discovery process. Eventually close to 100% of published study reports may show statistically significant results (31), although this is totally implausible given the fact that most of these studies are of small sample size and thus of limited power to show statistically significant associations with the outcomes of interest, if such associations exist at all.

Preregistration (32, 33) has been proposed also as a means to diminish the problem of inflated performance due to selective and nonreporting. An analysis of published diagnostic accuracy studies, which covers also biomarker studies, revealed that at present study reports rarely mention any registration number (27). In theory, studies of biomarkers can be preregistered like most other observational studies, unless there is some special reason to ensure confidentiality. However, in many cases the data have been already collected and it is impossible to know even whether they have already been analyzed by the time a study is registered. Such stealth postregistration masquerading as preregistration may offer false assurance.

The Standards for Reporting Diagnostic Accuracy (STARD) initiative, which received strong support from this journal, aimed at improving the completeness of reporting of diagnostic accuracy studies. An update was recently published (34). A metaanalysis of evaluations of the completeness in reporting have shown a significant, but small improvement after the release of STARD (29), but the median number of reported items is still about half of what STARD recommends.

Other reporting standards have been developed for diverse types of studies that may be relevant to biomarkers, including tumor biomarkers, prognostic models, multivariable risk models, and genetic risk scores (35–37). There are also published recommendations for the minimal standards of several types of biological measurements that may serve as biomarkers, formatted after the example of the Minimum Information About a Microarray Experiment (MIAME) recommendations for microarrays (38). The uptake of these standards has varied across fields.

Validation

Validation of biomarkers is a broadly used term that can encompass research practices with different connotations (39–41). The common denominator is trying to establish that the performance of the discovered biomarker is indeed credible, beyond what has been seen in an initial discovery study.

Replication of the biomarker performance in different samples and different studies is necessary to establish some minimal generalizability. However, most candidate biomarkers are assessed only once without any replication plan (42) and replication is not a readily fundable step for most funding programs.

Replication can be done with different levels of independence from the original study. Sometimes, the new studies may still be drawn from the same overarching sample collections and from the same study populations and analyses may be done by the same investigators. At the other end of the spectrum, entirely different, new populations may be assessed and the replication may be performed by entirely independent investigators.

Often there is a hybrid between these 2 extremes. Some samples may come from a known prestudied population and others from a different one. Some investigators may be among those making the original discovery and first publications, others may be their affiliates, and still others may have no link to the original team. Inbreeding at the level of study populations and/or investigators may tend to give more optimistic estimates of replicated performance.

The large majority of biomarker studies never have an independent replication. When they do, it is more frequent for investigators of the original team to be involved than not. Not surprisingly, the discriminating ability decreases when truly external independent validation is attempted (43). Performance in real-life clinical practice is likely to be more accurately reflected though by performance in external independent validation.

When multiple studies are performed, the best-informed picture of the evidence on the biomarker performance is offered by a careful systematic review and accompanying metaanalysis of all studies available. Systematic reviews of biomarkers, as prognostic or diagnostic tests, are becoming very popular (44–46). However, they are usually more useful in assessing the problems with the evidence rather than the evidence itself.

Larger studies typically tend to show less promising performance than smaller studies (47) which may nevertheless be highly cited and drive interest in the field owing to preferential citation bias for the most exciting results. Fig. 2 shows an empirical comparison of the results of the largest studies against the results of the most cited study for the set of the most cited biomarker studies in the biomedical literature.

Fig. 2.The largest studies find substantially smaller effects for biomarkers than the most cited studies on the same biomarkers.

Comparison of the relative risk in the largest study against the relative risk in the most highly cited study of the same biomarker for the most highly cited biomarkers in the biomedical literature (those with studies that have received over 400 citations). Closed and open circles refer to biomarkers where the highly cited study was published early or late. Redrawn with permission from JAMA from Ioannidis et al. (47).

Selective reporting of the results of biomarker studies also applies to the validation stage (48, 49). Studies with less promising results may not necessarily disappear entirely (classic “publication bias”). It is probably more common that investigators explore a large space of potential alternative analyses (some of them more legitimate than others) to corroborate a nice-looking performance for the biomarker. Common issues involve the selection of favorable subgroup or subset analyses and the manipulation of optimal cutoffs for continuous measurements (50–53).

Depending on the methodological challenges of each measurement platform, additional special problems may arise in the validation phase. These problems may result in incomplete, suboptimal validation analyses that yield inflated estimates of performance, as has been described in the case of markers based on microarray signatures (54) and may affect also proteomics (55).

Given these subtle biases, most, if not all, published biomarker studies may end up having statistically significant results, as mentioned above (31). Instead of being seen as a sign of perfect validation, this excess significance may be an indirect indicator of bias in the field and the need to perform some carefully done, large, preregistered studies (56).

Options to improve the quality and trustworthiness of the validation evidence include collaborative analyses in large consortia and multiple teams of investigators (57, 58). The paradigm has been popular in several areas of epidemiological research and is further facilitated by the advent of multiple biobanks with large sample capacity (59). The advantages may include the ability to standardize or harmonize processes and methods, procurement of large sample sizes, preagreement on a rigorous analytical plan, and avoidance of selective reporting.

Some collaborative efforts have shown convincingly the poor validity of single laboratory efforts and have led to realization that collaboration, standardization, and rigorous methods are needed (60). This process can transform the validity of entire fields. Sometimes consortia may be built with an expectation of moving promising biomarkers toward clinical implementation, but the multisite validation may show that the performance of these biomarkers is suboptimal and therefore one needs to go back to discovery again. The disadvantages of consortia include the need for substantial resources and difficulties arising in the coordination of large teams.

Collaborative consortia that use already collected and analyzed data have an extra caveat. When the results of single teams participating in a consortium are already available or can be glimpsed at quickly, e.g., by running cursory analyses on the spot, the large sample size may not protect from selection bias. In some cases, consortia may be built including the teams that already show the best performance for the biomarker in their study-specific analyses and they may also select analytical models that are already known to give the most spectacular results.

Clinical Translation

Clinical translation means that a biomarker moves beyond the stage of research testing and an effort is made to introduce it in clinical use. There is currently little rationale for deciding whether this step is taken or not. Some biomarkers are rushed into clinical translation based on limited evidence about their performance and analytical validity, limited or no external independent validation, and no systematic evaluation of the available evidence. Conversely, for other biomarkers dozens, if not hundreds of redundant studies are done, without ever crossing the border to clinical translation. For example, C-reactive protein was moved relatively fast to clinical practice for cardiovascular prediction, while subsequent evidence showed that it has little additional predictive ability (46). Conversely, dozens of microRNA biomarker studies are published (56) but there is yet no effort to clinically translate their potential.

Sometimes there is a strong drive and often a for-profit sponsor who is interested in promoting the biomarker. For example, some genetic test companies have long promoted tests that have limited validity and questionable or clearly no clinical use (61). In other cases, there is some disconnect of the researchers who perform these studies from clinical reality and no impetus to try to make some clinical sense of the biomarker.

At present, the translational road ends in publishing more and more papers. To correct this pattern, one needs to change the incentives for performing research. Instead of rewarding researchers only for publishing more papers in high-impact journals, one could also address the validity, reproducibility, and eventually the translational impact of the work done. (Our plea for a redesign of the biomarker pipeline below could help in this.)

Even for biomarkers that have been validated in good quality studies, with substantial external independent validation and a promising overall picture in 1 or more systematic reviews, clinical translation is not necessarily guaranteed or indicated. One needs to take into account not only whether the evidence as presented is reliable (as discussed above), but also what evidence exists for other biomarkers that may be more suitable and may have more reliable and promising evidence for introduction in clinical practice. This would require overarching umbrella reviews (62) of the entire biomarker research agenda in a given field of potential clinical application. Umbrella reviews of observational studies can be thought of as systematic reviews of systematic reviews. They offer the opportunity to assess all factors of interest in a given field (biomarkers, risk factors, existing tests, etc.) in a systematic fashion and identify which ones have the strongest evidence. Those with the strongest evidence may then be considered for clinical potential. Such approaches are in their infancy in biomarker research and typically each biomarker is pursued in isolation from others, often by some committed investigators or by some interested for-profit sponsors.

Evaluation

Increasingly healthcare payers will only be prepared to cover the investments and other costs of biomarker-based assays and technologies (63) if they have been properly evaluated, and shown to lead to improved patient outcomes at acceptable costs. Clinical applications of biomarkers may include diagnosis, assisting the choice of treatment, or monitoring of disease progression risk, to name a few. Biomarkers may be used as single markers or as part of larger panels, models, or scores.

In all of these applications, a good discriminating performance of a biomarker after discovery and validation does not guarantee clinical utility (64). A biomarker with poor discrimination is unlikely to have clinical utility, but one with very accurate discrimination may still not be useful. Consideration of the prospects to change clinical outcomes is usually speculative, often depending on theory or wishful thinking.

In principle, one could perform randomized trials (65) where a biomarker test is used in one arm and not used in another. At present, there are very few randomized trials of diagnostic/prognostic/predictive tests, in general, (65, 66), in the range of few hundreds (<0.1% of all randomized trials performed on all interventions, a number that now exceeds half a million). Those involving biomarkers are only a small fraction of them. Empirical assessment of trials of diagnostic tests has shown that there is very poor concordance between the different families of outcomes, i.e., the effect on the use of other diagnostics, the effect on use of different treatments and the effect on clinical endpoints (65).

The vast majority of biomarkers have not been assessed for their ability to improve outcomes in randomized trials. This adds another note of caution as to their utility. Markers that serve primarily informational purposes may not necessarily be useless, but information alone may be of uncertain value. Information-seeking that may sound as satisfying curiosity can even be harmful per se (67).

While cancer is the most promising field to-date for the use of targeted treatments, 1 randomized trial shows that the promise is probably overstated and that relatively few cancer patients can expect a clear benefit by targeted treatments that depend on biomarkers (68).

More and better evaluations of biomarkers are urgently needed, but randomized trials of tests can sometimes be inefficient or even misleading (69). We need a better understanding of the necessary evidence requirements to show that tests are effective in improving patient-relevant health outcomes (70).

Guidelines for the use of biomarkers (71) try to compile evidence across multiple dimensions and not surprisingly they may reach different conclusions, as it has been empirically documented in comparison of guidelines for genetic biomarkers (72). The regulatory landscape for the approval of biomarkers is not yet as well defined as it is for drugs and biologics. Two typical pathways in the US are a CLIA regulatory strategy or a Food and Drug Administration (FDA) regulatory strategy. The processes have generally high technical standards, but proof of effectiveness from trials is not typically required.

Implementation and Deimplementation

Implementation refers to how the biomarker is actually widely deployed for use in real life settings (73). Successful implementation may require addressing several practical considerations, including, but not limited to, the use of sophisticated measurement technology in suboptimal settings, performance of the tests under conditions that are far from those where marker performance was optimized, use in patient populations that are very different sometimes from those where the marker has been established, and integration of the testing routine in diverse healthcare systems. It is possible that all these considerations may add restrictions to the use of an otherwise valid biomarker that has even demonstrated clinical utility under controlled settings. Some biomarkers, like any other medical technology, may not be possible to deploy everywhere with equal benefits (74).

Deimplementation refers to the process with which a biomarker that has reached the stage of clinical care and it is already used in real settings is proven to be not useful and thus has to be abandoned (75). This change may be dictated by the accrual of additional evidence: new studies that show inferior accuracy, randomized trials that show no benefits, or studies that demonstrate more harms than anticipated (e.g., overdiagnosis, wrong diagnoses, and more serious repercussions from erroneous results). It may also happen when the existing evidence is reappraised in guidelines, where previous conflicts of interest are better contained, and thus it is candidly acknowledged that the biomarker is not really as useful as previously thought or is found to have more limited setting-specific utility. Prostate-specific antigen (PSA) is a classic example in this regard (76).

Deimplementation is difficult to accomplish and it is likely to meet with substantial resistance for multiple reasons. These include inertia, the difficulty to change practices that have been entrenched in healthcare and are recompensed by payers, and conflicts of interest from specialty practitioners or sponsors who stand to benefit from the continued use of the biomarker. Nevertheless, it is possible that biomarkers that are currently in use (like much of other diagnostic or prognostic/predictive technology) may need to be deimplemented (77). The reason is that the introduction of these markers in clinical care in the past was done at a time when there were less consensus and less complete understanding of optimal standards for testing, validating, and proving their clinical utility.

Pipeline Construction

The biomarker pipeline is typically described as a series of connected pipes, through which markers are discovered, then move to validation, translation, and evaluation, after which implementation in practice should follow (Fig. 3A). This metaphoric representation may in itself also be slightly optimistic, and this simplified view on biomarkers and medical tests may be partially responsible for its overall failure to deliver, as many discovered biomarkers fail to meet the demands of the clinic (4–6, 78).

Fig. 3.Schematic diagrams of the biomarker pipeline: (A), Current situation: a tortuous series of linearly connected pipes. (B), A circular acceleration pipeline.

The basic assumption in the discovery phase is that it suffices to find a biomarker, which can then, through a subsequent series of processes, be moved through the pipeline and transformed into a validated and accepted medical test. Yet biomarkers, by themselves, rarely lead to improved patient outcomes. The relation between medical testing and patient outcomes is, in most cases, an indirect one; patient outcomes will only be improved if the results from testing are used to guide downstream clinical actions, such as the decision to start, stop, or modify treatment, to order additional tests or to refrain from doing so, or to discuss or initiate other actions with the tested patient.

This observation has 2 consequences. First, one has to identify how a new biomarker-based test can change existing clinical pathways. There are several possible options (79). The new test can replace current tests (e.g., mass spectrometry may replace immunoassays (80) and the benefits could come from improved speed or increased accuracy. Alternatively, new biomarker-based tests can be used as triage tests before existing, more expensive or cumbersome tests, or as add-ons, after existing tests. New biomarker-based tests can be linked through current management options if it can be shown that patients who now undergo treatment should not, because they do not benefit, or those who currently are not treated should definitely be, because of the presence of a clear benefit despite previous beliefs of the contrary. This is the promise of stratified or precision medicine.

Rather than having evaluation of the clinical use of biomarkers as a prefinal step, it is probably better to start with an analysis of current pathways, and look for areas where the addition or insertion of biomarker-based tests can lead to substantial improvements in patient outcome at acceptable additional costs, or reduce costs and simplify healthcare without compromising outcomes.

Such initial explorations can have at least 2 major corollaries for biomarker research. First—and probably most important—it will help identify the patient subpopulation of interest. Rather than searching for biomarkers in ill-defined all-inclusive phenotypes (“dementia,” “metabolic syndrome”), using extreme cases and healthy controls, we can search for markers in a clinical setting and stage of development where changes can be expected (“recently detected,” “failing to respond to standard therapy,” “family member of”), within the context of well-described alternative management options.

The second corollary from the consideration of the clinical expectations and consequences is that it can lead to the a priori specification of minimally acceptable criteria for the clinical and analytical performance of novel biomarkers. A significant association between a marker, with discrimination between extreme phenotypes and healthy controls, is rarely sufficient for clinical utility; it can lead to a hopelessly futile process of validation and evaluation. While associations can be useful to test hypotheses about etiology and pathophysiology or mechanisms of diseases, a shotgun approach at biomarker discovery will only generate multiple leaks in the biomarker pipeline, leading to avoidable waste.

A working group of the European Federation of Clinical Chemistry and Laboratory Medicine has recently released a similar plea for biomarker development targeting unmet clinical needs (81). The group released a 14-item checklist and suggested clinical pathway mapping to identify clinical management decisions linking biomarker testing.

Instead of a linear pipeline, we probably need a circular system, a “biomarker accelerator,” one that has critical analysis of applications, of current clinical pathways, as a central element, and moves from there to the identification of markers that meet predefined performance criteria, inform the other phases, and back again to clinical application (Fig. 3B). Very early in the biomarker development process, the intended purpose of testing and the role of the biomarker in the clinical management pathway are defined. From this definition minimally acceptable criteria for analytical and clinical performance are derived. Then, if a potential biomarker is identified (Fig. 3B, right), it needs to be validated against these criteria: does it have the required level of performance (Fig. 3B, bottom)? Thereafter its effectiveness—the extent to which it actually improves meaningful outcomes—has to be evaluated in clinical trials, randomized, or otherwise (Fig. 3B, left). If it does, the assay or test based on the biomarker can be implemented and applied in practice, if feasibility and other additional concerns are absent. If not, the cycle has to start over again, probably with modifications. All of this has to be done with strong methods, informative and complete reporting, in collaborative trials, which have to be numerous and large enough. Such a circular development process may generate better results than the current linear ones.

Putting It Together

Table 1 summarizes the key reasons for failures of the biomarker pipeline in each stage along with some possible solutions. Some of the solutions are easier to implement than others. Most solutions would also benefit from a concerted action of scientists, funders, other sponsors (including the industry), and perhaps other stakeholders, e.g., journals, reimbursing agencies, professional societies, institutions, and entrepreneurs. Goals may need to be set also on what the expectations are from various research investments into biomarker research, i.e., what would be considered a success after say 5 or 10 years of investment. Despite a long track record of failures and wasted effort, biomarker research may still deliver some truly useful biomarkers.

Problems and potential solutions at each stage of the biomarker research pipeline.

Some of the suggestions to improve the pipeline may involve more scrutiny of studies and the total evidence. This scrutiny has the risk of stifling innovation if some avenues of investigation are terminated too early. Experts may also exert an inhibiting influence. In all, evidence-based approaches should counter inadvertent expert influence rather than be used to provide an alibi for expert power.

Laboratory professionals do not always get sufficient credit for the benefits their expertise and their technology can bring to patient care, and diagnostics fall behind in visibility and recognition, compared to therapeutics in general and pharmaceuticals in particular. Yet unless one starts to recognize the unique nature and requirements of medical tests, and the typical nature and the sources of waste in biomedical research, it seems inevitable that not more than a few, rapidly dissipating drops will fall out at the end of the current biomarker pipeline.

Footnotes

Author Contributions:All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article.

. Evaluating the quality of research into a single prognostic biomarker: a systematic review and meta-analysis of 83 studies of C-reactive protein in stable coronary artery disease. PLoS Med2010;7:e1000286.

. From biomarkers to medical tests: the changing landscape of test evaluation. Clin Chim Acta2014;427:49–57. Organizational author: Test Evaluation Working Group of the European Federation of Clinical Chemistry and Laboratory Medicine.